APPLICATION OF ARTIFICIAL INTELLIGENCE AND REMOTE SENSING TECHNOLOGIES IN EARLY PLANT DISEASE DETECTION

This article comprehensively analyzes the scientific and practical potential of artificial intelligence and remote sensing technologies in the early detection of plant diseases. The study examines the significance of modern digital monitoring methods, including multispectral imaging, drone technologies, satellite data analysis, and machine learning algorithms in the fields of botany and agriculture. The primary objective of the research is to evaluate the effectiveness of automated diagnostic systems based on artificial intelligence in identifying the early stages of plant diseases, particularly in conditions where visual detection by humans is limited or inefficient. The findings demonstrate that remote sensing technologies significantly improve the ability to monitor the physiological condition of plants in real time, detect disease symptoms at an early stage, and forecast factors that may negatively affect crop productivity. Furthermore, the study scientifically confirms that systems operating on convolutional neural networks (CNNs), machine learning, and deep learning models can substantially increase the accuracy of plant disease detection.